[R] Account for a factor variability in a logistic GLMM in lme4

Jim Lemon drj|m|emon @end|ng |rom gm@||@com
Tue Sep 4 00:36:22 CEST 2018

Hi Pedro,
I have encountered similar situations in a number of areas. Great care
is taken to record significant events of low probability, but not the
non-occurrence of those events. Sometimes this is due to a problem
with the definition of non-occurrence. To use your example, how close
does an animal have to approach the crossing to be counted as not
crossing? Perhaps it was just a failure to record the species of
animals that didn't cross. In that case you have a problem, because
the probability of crossing within species cannot be estimated from
the data you describe.

On Tue, Sep 4, 2018 at 12:43 AM Pedro Vaz <zasvaz using gmail.com> wrote:
> We did a field study in which we tried to understand which factors
> significantly explain the probability of a group of animals (5 species in
> total) crossing through 30 wildlife road-crossing structures. The response
> variable is binomial (yes=crossed; no = did not cross) and was recorded by
> animal species. We did about 30 visits to each crossing structure (our
> random factor) in which we recorded the binomial response by each animal
> species and the values of a few predictors.
> So, I have this (simplified for better understanding) mixed effects model:
> library (lme4)
> Mymodel <- glmer(cross.01 ~ stream.01 + width.m + grass.per + (1|structure.id),
>   data = Mydata, family = binomial)
> stream is a factor with 2 levels; width.m is continuous; grass.per is a
> percentage
> This is the model in which I assessed crossings by all species combined
> (i.e., cross. 01 = 1 when an animal of any species crossed, cross.01 = 0
> when no animal crossed). However, we did one model per species and those
> species-specific models highlight that different species exhibit different
> relationships between crossings and explanatory variables.
> My problem: This means that my model above suffers from an additional
> source of variation related to the species level without accounting for it.
> However I cannot recalibrate the above model adding the species level as
> random factor because, in my binomial response, the zero means no species
> crossed (all zeros would have "NA" or, say, "none" for species) and so that
> additional source of variation is only present when the response was 1.
> Just to confirm this, I did add species as a random factor:
> (1 | structure.id) + (1 | species)
> As expected, the message is "Error: Response is constant"
> How can I account for the species variability in my model in lme4?
> A few more details:
> A few more details:
> - I had 5 mammal species crossing through the 30 road-crossing structures.
> In 134 occasions (i.e., 134 of my records on individual
> crossing-structures), no animal crossed (so, @Dimitris Rizopoulos, no, I
> didn't have the species of the animals which did not cross. A "no cross"
> was a "zero" for that visit to the crossing-structure). In 498 occasions,
> at least one animal of a given species crossed the structure (these were my
> "ones" in my logistic response)
> - A side comment: This is to respond to a reviewer in a paper of mine,
> i.e., I did and presented species-specific and "all combined species"
> models in the draft reviewed but now the reviewer is asking me to control
> for the species variability in the "combined species model". He asked me to
> include a random factor but I realized that is not possible since all my
> zeros would have "none" for the species that crossed. So, is it possible to
> control for the species variability in my model in lme4 in another way? I
> know in nlme including a fitting of variance structures it's not that
> difficult...
> - Every time an animal crossed, the binary response was "one" and I
> recorded the animal species as well. Thus, I have variability between
> species in the "ones" but not in my "zeros" of my logistic model.
>         [[alternative HTML version deleted]]
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